Federated Learning for UAVs-Enabled Wireless Networks: Use Cases, Challenges, and Open Problems

The use of Unmanned Aerial Vehicles (UAVs) for wireless networks is rapidly growing as key enablers of new applications, including: surveillance and monitoring, military, delivery of medical supplies, telecommunications, etc. In particular, due to their unique proprieties such as flexibility, mobility, and adaptive altitude, UAVs can act as mobile base stations to improve capacity, coverage, and energy efficiency of wireless networks. On the other hand, UAVs can operate as mobile terminals to enable many applications such as item delivery and real-time video streaming. In such context, data-driven Deep Learning-assisted (DL) approaches are gaining a growing interest to not only exploit the huge amount of generated data, but also to optimize the network operations, and hence ensure the QoS requirements of these emerging wireless networks. However, UAVs are resource-constrained devices especially in terms of computing and power resources, and traditional DL-assisted schemes are cloud-centric, which require UAVs’ data to be sent and stored in a centralized server. This represents a critical issue since it generates a huge network communication overhead to send raw data towards the centralized entity, and hence may lead to network bandwidth and energy inefficiency of UAV devices. In addition, the transferred data may contain personnel data such as UAVs’ localization and identity, which can directly affect UAVs’ privacy concerns. As a solution, Federated Deep Learning (FDL), or distributed DL, was introduced, where the basic idea is to keep raw data where it is generated, while sending only users’ local trained DL models to the centralized entity for aggregation. Due to its privacy-preserving and low communication overhead and latency, FDL is much more adequate for many UAVs-enabled wireless applications. In this work, we provide a general introduction of FDL application for UAV-enabled wireless networks. We first introduce the FDL concept and its fundamentals. Then, we highlight the possible applications of FDL in UAVs-enabled wireless networks by addressing the suitability and how to use FDL to deal with target challenges. Finally, we discuss about key technical challenges, open issues, and future research directions on FDL-based approaches in such context.

[1]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[2]  Basabi Chakraborty,et al.  A novel normalization technique for unsupervised learning in ANN , 2000, IEEE Trans. Neural Networks Learn. Syst..

[3]  Navrati Saxena,et al.  Next Generation 5G Wireless Networks: A Comprehensive Survey , 2016, IEEE Communications Surveys & Tutorials.

[4]  Kin K. Leung,et al.  Adaptive Federated Learning in Resource Constrained Edge Computing Systems , 2018, IEEE Journal on Selected Areas in Communications.

[5]  Walid Saad,et al.  Mobile Unmanned Aerial Vehicles (UAVs) for Energy-Efficient Internet of Things Communications , 2017, IEEE Transactions on Wireless Communications.

[6]  Rui Zhang,et al.  UAV-Aided Offloading for Cellular Hotspot , 2017, IEEE Transactions on Wireless Communications.

[7]  Ying-Chang Liang,et al.  Federated Learning in Mobile Edge Networks: A Comprehensive Survey , 2020, IEEE Communications Surveys & Tutorials.

[8]  Walid Saad,et al.  Federated Learning for Ultra-Reliable Low-Latency V2V Communications , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[9]  Jürgen Schmidhuber,et al.  Learning to Forget: Continual Prediction with LSTM , 2000, Neural Computation.

[10]  Dit-Yan Yeung,et al.  Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.

[11]  Wansu Lim,et al.  Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions , 2019, IEEE Access.

[12]  Ursula Challita,et al.  Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial , 2017, IEEE Communications Surveys & Tutorials.

[13]  Walid Saad,et al.  Communications and Control for Wireless Drone-Based Antenna Array , 2017, IEEE Transactions on Communications.

[14]  Walid Saad,et al.  A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems , 2018, IEEE Communications Surveys & Tutorials.

[15]  Walid Saad,et al.  Network Formation in the Sky: Unmanned Aerial Vehicles for Multi-Hop Wireless Backhauling , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[16]  Qiang Yang,et al.  Federated Deep Reinforcement Learning , 2019, 1901.08277.

[17]  Sarvar Patel,et al.  Practical Secure Aggregation for Federated Learning on User-Held Data , 2016, ArXiv.

[18]  Solmaz Niknam,et al.  Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges , 2019, IEEE Communications Magazine.

[19]  S. H. Alsamhi,et al.  Survey on Collaborative Smart Drones and Internet of Things for Improving Smartness of Smart Cities , 2019, IEEE Access.

[20]  Blaise Agüera y Arcas,et al.  Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.

[21]  Hubert Eichner,et al.  Towards Federated Learning at Scale: System Design , 2019, SysML.

[22]  Osvaldo Simeone,et al.  A Very Brief Introduction to Machine Learning With Applications to Communication Systems , 2018, IEEE Transactions on Cognitive Communications and Networking.

[23]  Walid Saad,et al.  Caching in the Sky: Proactive Deployment of Cache-Enabled Unmanned Aerial Vehicles for Optimized Quality-of-Experience , 2016, IEEE Journal on Selected Areas in Communications.